Large-scale Cognitive Radio Networks Research Articles

An Energy-Efficient Cross-Layer Routing Protocol for Cognitive Radio Networks Using Apprenticeship Deep Reinforcement Learning

Deep reinforcement learning (DRL) has been successfully used for the joint routing and resource management in large-scale cognitive radio networks. However, it needs lots of interactions with the environment through trial and error, which results in large energy consumption and transmission delay. In this paper, an apprenticeship learning scheme is proposed for the energy-efficient cross-layer routing design. Firstly, to guarantee energy efficiency and compress huge action space, a novel concept called dynamic adjustment rating is introduced, which regulates transmit power efficiently with multi-level transition mechanism. On top of this, the Prioritized Memories Deep Q-learning from Demonstrations (PM-DQfD) is presented to speed up the convergence and reduce the memory occupation. Then the PM-DQfD is applied to the cross-layer routing design for power efficiency improvement and routing latency reduction. Simulation results confirm that the proposed method achieves higher energy efficiency, shorter routing latency and larger packet delivery ratio compared to traditional algorithms such as Cognitive Radio Q-routing (CRQ-routing), Prioritized Memories Deep Q-Network (PM-DQN), and Conjecture Based Multi-agent Q-learning Scheme (CBMQ).

Opportunistic Spectrum Sharing With Wireless Energy Transfer in Stochastic Networks

We consider underlay spectrum sharing with wireless energy transfer in a large-scale cognitive radio network, where each primary user (PU) can harvest radio frequency energy from its associated access point (AP). An energy cooperation zone is applied around each PU, wherein the secondary user (SU) with the best channel quality toward PU is selected to cooperatively transfer wireless energy. With SUs’ assistance, each PU can harvest a predefined amount of energy in a shorter time, so there will be more concurrent primary links in the network. We analyze the transmission probability of PUs by properly modeling their energy statuses using Markov chain. Furthermore, a guard zone is applied around each active AP to prohibit the nearby SU transmissions to avoid strong interference, and SUs outside the guard zones of all the active APs can access the spectrum opportunistically. Under the constraint that the area throughput of primary system should be improved by at least a certain degree, the area throughput of the secondary system is maximized by jointly determining the SUs’ density and the guard zone radius. Numerical results show that our scheme can well accommodate SUs’ transmissions while guaranteeing PUs’ performance requirement.

Spectrum Mapping in Large-Scale Cognitive Radio Networks With Historical Spectrum Decision Results Learning

To mitigate the contradiction between scarcity spectrum resources and heavily wireless services, cognitive radio (CR) has been proposed to improve spectrum utilization through allowing CR users to access licensed channels opportunistically. In large-scale networks, spectrum status is not the same at different locations due to the heterogeneity of CR network (CRN). To cope with such heterogeneity, some noncooperative spectrum sensing and distributed cooperative sensing algorithms were proposed. Such spectrum decision results will be exploited in this paper, in order to draw the spectrum map of the entire large-scale CRN. The proposed spectrum mapping scheme contains three processing steps, and a boundary CR users searching algorithm using kennel function based supportive vector machine is adopted to improve the performance of the proposed scheme. The simulation results show that radial basis function kennel performs the best in the proposed scheme, and when accuracy threshold ${\theta _{a}} = 0.95$ and filtration threshold ${\theta _{f}} = 0.02$ , the proposed scheme can draw a spectrum map with the accuracy 99.3% using only about 28% CR users performing spectrum sensing. Furthermore, the number of CR users and energy detection threshold have little effects on the performance of the proposed scheme.

Cooperative spectrum estimation over large‐scale cognitive radio networks

Spectrum sensing is a significant issue in cognitive radio networks which enables estimation of the frequency spectrum and hence provides frequency reuse. In the large-scale cognitive radio networks, secondary users cannot share a common spectrum since the coverage area of primary users is limited. In this study, the authors suggest a diffusion adaptive learning algorithm based on correntropy cooperation policy, which first categorises received data of secondary users into several groups, and then learns a common spectrum inside each group. The mean-square performance of proposed algorithm is analysed and supported by simulations. Experimental results show that, in a multitask cognitive network, the proposed algorithm can achieve a better mean-square deviation learning performance both in transient and steady-state regimes in comparison with other conventional algorithms.

Increasing throughput in energy-based opportunistic spectrum access energy harvesting cognitive radio networks

The performance of large-scale cognitive radio (CR) networks with secondary users sustained by opportunistically harvesting radio-frequency (RF) energy from nearby primary transmissions is investigated. Using an advanced RF energy harvester, a secondary user is assumed to be able to collect ambient primary RF energy as long as it lies inside the harvesting zone of an active primary transmitter (PT). A variable power (VP) transmission mode is proposed, and an energy-based opportunistic spectrum access (OSA) strategy is considered, under which a secondary transmitter (ST) is allowed to transmit only if its harvested energy is larger than a predefined transmission threshold and it is outside the guard zones of all active PTs. The transmission probability of the STs is derived. The outage probabilities and the throughputs of the primary and the secondary networks, respectively, are characterized. Compared with prior work, the throughput can be increased by as much as 29%. The energy-based OSA strategy can be generally applied to a non-CR setup, where distributed power beacons (PBs) are deployed to power coexisting wireless signal transmitters (WSTs) in a wireless powered sensor network.

On Topology and Resilience of Large-Scale Cognitive Radio Networks Under Generic Failures

It has been demonstrated that in wireless networks, blackholes, which are typically generated by isolated node failures, and augmented by failure correlations, can easily result in devastating impact on network performance. In order to address this issue, we focus on the topology of Cognitive Radio Networks (CRNs) because of their phenomenal benefits in improving spectrum efficiency through opportunistic communications. Particularly, we first define two metrics, namely the failure occurrence probability $p$ and failure connection function $g(\cdot) $ , to characterize node failures and their spreading properties, respectively. Then we prove that each blackhole is exponentially bounded based on percolation theory. By mapping failure spreading using a branching process, we further derive an upper bound on the expected size of blackholes. With the observations from our analysis, we are able to find a sufficient condition for a resilient CRN in the presence of blackholes through analysis and simulations.

Intelligent Cooperative Spectrum Sensing via Hierarchical Dirichlet Process in Cognitive Radio Networks

Cognitive radio (CR) is a critical technology for improving spectrum utilization and solving the radio spectrum scarcity problem. In CR devices, spectrum sensing is important to implement opportunistic spectrum access. Many spectrum sensing schemes have been proposed, including uncooperative, cooperative, centralized, and distributed algorithms. However, they aimed to obtain a global consensus sensing result, which may not always be possible in large-scale cognitive radio networks (CRNs) due to heterogeneous spectrum availability in different areas. Hence, some new spectrum sensing schemes should be designed to discover idle heterogeneous spectrum in CRNs. In this paper, we propose an intelligent cooperative spectrum sensing algorithm based on a non-parametric Bayesian learning model, namely the hierarchical Dirichlet process, which groups spectrum sensing data without the need to know the number of hidden spectrum states, and discovers a common sparse spectrum within each group. Furthermore, a concisely distributed information exchange scheme is designed, where intra-cluster and inter-cluster spectrum information is shared for global spectrum cognition. Experimental results show that the proposed algorithm can exploit the spatial relationship among sensed data to achieve a better spectrum sensing performance in terms of detection probability and false alarm probability.

Bounds on Secondary User Connectivity in Cognitive Radio Networks

We consider a large-scale cognitive radio network (CRN) in a Rayleigh fading environment. Errors of spectrum sensing by secondary users (SUs) are considered in the letter. Cumulative inter-network interference (between primary and secondary networks) is calculated and outage probability of primary users (PUs) is derived in the closed-form. The upper and lower bounds on the percolation area are obtained and analyzed, within which the secondary network percolates while the PU's outage probability is kept below a tolerable threshold. The bounds depend on SU's correct detection and false alarm probabilities, and PU's transmit power and protection radius. Further we find that the percolation area that percolates SUs and meanwhile provides protection for PUs does not always exist.

Analysis and Optimization of Sensing Reliability for Relay-Based Dual-Stage Collaborative Spectrum Sensing in Cognitive Radio Networks

Aiming at multiuser cognitive radio networks in a harsh electromagnetic environment, cooperative spectrum sensing with single cooperation can hardly achieve to the desired detection performance. In this paper, a relay-based dual-stage collaborative spectrum sensing model (DCSS) that combines the distributed method with the centralized method is proposed. Furthermore, the optimality of the detection performance of DCSS is investigated in an efficient and feasible way. The optimal voting rule value and the optimal energy detection threshold are also derived by minimizing the detection error rate of the entire network. Finally, an efficient fast sensing algorithm for a large-scale cognitive radio network is deduced, which requires the minimal number, and not all, of cognitive radio users for DCSS while satisfying the target detection error rate bound. The simulated results indicate that when compared to the normal single cooperation method, the optimized DCSS method can reduce the number of cognitive radio user by 65 % while still meeting the detection error rate requirement of less than 1 %.

Proportional and double imitation rules for spectrum access in cognitive radio networks

In this paper, we tackle the problem of opportunistic spectrum access in large-scale cognitive radio networks, where the unlicensed Secondary Users (SUs) access the frequency channels partially occupied by the licensed Primary Users (PUs). Each channel is characterized by an availability probability unknown to the SUs. We apply population game theory to model the spectrum access problem and develop distributed spectrum access policies based on imitation, a behavior rule widely applied in human societies consisting of imitating successful behaviors. We develop two imitation-based spectrum access policies based on the basic Proportional Imitation (PI) rule and the more advanced Double Imitation (DI) rule given that a SU can only imitate the other SUs operating on the same channel. A systematic theoretical analysis is presented for both policies on the induced imitation dynamics and the convergence properties of the proposed policies to the Nash equilibrium. Simple and natural, the proposed imitation-based spectrum access policies can be implemented distributedly based on solely local interactions and thus is especially suited in decentralized adaptive learning environments as cognitive radio networks.

Increasing sum-rate in large-scale cognitive radio networks by centralized power and spectrum allocation

We revisit the widely investigated problem of maximizing the centralized sum-rate capacity in a cognitive radio network. We consider an interference-limited multi-user multi-channel environment, with a transmit sum-power constraint over all channels as well as an aggregate average interference constraint towards multiple primary users. Until very recently only sub-optimal algorithms were proposed due to the inherent non-convexity of the problem. Yet, the problem at hand has been neglected in the large-scale setting (i.e., number of nodes and channels) as usually encountered in practical scenarios. To tackle this issue, we first propose an exact mathematical adaptation of the well-known successive convex geometric programming with condensation approximations (SCVX) to better cope with large systems while keeping the convergence proof intact. Alternatively, we also propose a novel efficient low-complexity heuristic algorithm, ELCI. ELCI is an iterative approach, where the constraints are handled alternately based on the special property of the optimal solution, with a particular power update formulation based on the KKT conditions of the problem. In order to demonstrate ELCI’s efficiency we compare it to two state-of-the-art algorithms, SCVX, and the recently proposed global optimum approach, MARL. The salient highlight of ELCI is the relatively fast and very good sub-optimal performance in large-scale CR systems.

Percolation Degree of Secondary Users in Cognitive Networks

A cognitive network refers to the one where two overlaid structures, called primary and secondary networks coexist. The primary network consists of primary nodes who are licensed spectrum users while the secondary network comprises unauthorized users that have to access the licensed spectrum opportunistically. In this paper, we study the percolation degree of the secondary network to achieve k-percolation in large scale cognitive radio networks. The percolation degree is defined as the number of nearest neighbors for each secondary user when there are at least k vertex-disjoint paths existing between any two secondary relays in the percolated cluster. The percolated cluster is formed when there are an infinite number of mutually connected secondary users spanning the whole network. Each user in the cluster is possibly connected to several neighbors, inducing more communication links between any two of them. Since nodes located near the boundary have fewer neighbors, the boundary effect becomes a bottleneck in determining the percolation degree. For cognitive networks, when the primary node density becomes considerably large, the boundary effect spreads inside the network. The transmission area of most secondary users who are located near the primary nodes decreases due to the restriction of the primary network. Therefore, to ensure k-connectivity in the percolated cluster, each secondary user must be connected to more neighbors, and the percolation degree of the secondary network yields a function of the primary node density. We specify the relationship into three regimes regarding the topology variation of the cognitive network. A closed-form expression of the percolation degree under different primary node densities is presented. The expression characterizes the connectivity strength in the secondary percolated cluster, therefore providing analytical insight on fault tolerance improvement in cognitive networks.

Towards A Large-Scale Cognitive Radio Network Testbed: Spectrum Sensing, System Architecture, and Distributed Sensing

This paper presents a comprehensive review of the cognitive radio network (CRN) testbed built at TTU. Our goals are (1) to use our CRN testbed as a data acquisition tool; (2) to use random matrix theory to model the collect data and apply the new models in the context of quantum information. We attempt to achieve a balance between experimental work and theoretical work. We first spell out the vision and concrete tasks for our research in the near future. Second, we review our latest results in an more accessible manner than the conference version.

Accumulative Interference Modeling for Distributed Cognitive Radio Networks

A Cognitive Radio (CR) network should be able to sense its environment to adapt its communication so that it can utilize unused licensed spectrum without interfering with incumbent users. Properly modeling the expected interference from the entire CR network is therefore very important to effectively protect these incumbent users. We model the accumulative interference generated from a large-scale CR network and investigate how the CR network density affects the sensing requirements of the CRs to meet an interference constraint. More specifically, our model considers the impact of discrete network topology, the impact of imperfect sensing and the impact of collisions when the CR uses a distributed channel access scheme. We then apply our model to a CR network based on the IEEE 802.11 standard. We show that the collisions occurring frequently in these networks only have a small on the sensing requirements to protect the incumbent network.